CHILES. VII. Deep Imaging for the CHILES Project, an SKA Prototype

Radio astronomy is undergoing a renaissance, as the next generation of instruments provides a massive leap forward in collecting area and therefore raw sensitivity. However, to achieve this theoretical level of sensitivity in the science data products, we need to address the much more pernicious systematic effects, which are the true limitation. These become all the more significant when we consider that much of the time used by survey instruments, such as the Square Kilometre Array (SKA), will be dedicated to deep surveys. CHILES is a deep H i survey of the COSMOS field, with 1000 hr of Very Large Array time. We present our approach for creating the image cubes from the first epoch, with discussions of the methods and quantification of the data quality from 946 to 1420 MHz—a redshift range of 0.5−0. We lay out the problems we had to solve and describe how we tackled them. These are important because CHILES is the first deep wide-band multiepoch H i survey and has relevance for ongoing and future surveys. We focus on the accumulated systematic errors in the imaging, as the goal is to deliver a high-fidelity image that is only limited by the random thermal errors. To understand and correct these systematic effects, we ideally manage them in the domain in which they arise, and that is predominately the visibility domain. CHILES is a perfect test bed for many of the issues we can expect for deep imaging with the SKA or ngVLA, and we discuss the lessons we have learned.

Application of the digital model thermal errors of machine tools in automated production

The article presents a predicting method for a machine tool thermal error based on a nonlinear autoregressive neural network with an external input, as well as methods for smoothing experimental data obtained from measuring devices by approximation using polynomial regression and the gray systems theory. The development of accurate and robust thermal models is a critical step in achieving high productivity in thermal deformation reduction techniques on machine tools. Because thermal deformations of the machine structure caused by temperature increase often lead to thermal errors and reduce the accuracy of machining parts. The use of neural networks is a promising direction in solving forecasting problems. The authors propose a block diagram of a thermal process digital twin based on a neural network, which can be used in automated production. The results of the experiment carried out for the machine model 400V are obtained in the form of an assessment of approximation quality and accuracy of the forecasting model. The results show that the use of the proposed smoothing methods and a model for predicting a machine tool thermal error based on a neural network can improve the forecast accuracy.

Thermal error analysis of taurenEDM machine tool based on FCM fuzzy clustering and RBF neural network

A method that combines temperature field detection, adaptive FCM (Fuzzy c-means) clustering algorithm and RBF (Radial basis function network) neural network model is proposed. This method is used to analyze the thermal error of the spindle reference point of the taurenEDM (Electro-discharge machining) machine tool. The thermal imager is used to obtain the temperature field distribution of the machine tool while the machine tool simulates actual operating conditions. Based on this, the arrangement of temperature measurement points is determined, and the temperature data of the corresponding measurement points are got by temperature sensors. In actual engineering, too many temperature measurement points can cause problems such as too high cost, too much wiring. And normal processing can be affected. In order to establish that the thermal error prediction model of the machine tool spindle reference point can meet the actual engineering needs, the adaptive FCM clustering algorithm is used to optimize the temperature measurement points. While collecting the temperatures of the optimized temperature measurement points, the displacement sensors are used to detect the thermal deformation data in X, Y, Z directions of the spindle reference position. Based on the test data, the RBF neural network thermal errors prediction model of the machine tool spindle reference point is established. Then, the test results are used to verify the accuracy of the thermal errors analysis model. The research method in this paper provides a system solution for thermal error analysis of the taurenEDM machine tool. And this builds a foundation for real-time compensation of the machine tool’s thermal errors.

Research on Thermal Characteristics of Ball Screw Feed System Considering Nut Movement

The heat generated by the ball screw feed system will produce thermal errors, which will cause the positioning accuracy to decrease. The thermal simulation modeling of the ball screw feed system is the basis for compensating thermal errors. The current thermal characteristic modeling method simplifies the reciprocating movement of the nut pair on the screw shaft to varying degrees, which leads to a decrease in simulation accuracy. In this paper, the nut is regarded as a moving heat source, and a novel method is adopted to make the moving process of the heat source closer to the actual nut movement process. The finite difference method is used to simulate the temperature field and thermal error of the ball screw feed system under different working conditions. Firstly, based on the heat transfer theory, the heat conduction differential equation of the feed system is established and discretized. The thermal error model of the ball screw feed system is established. Then, the relationship between nut heat source position and operating time is established to simulate nut reciprocating motion. Finally, the temperature and thermal error experiments of the ball screw feed system were carried out, and the temperature experiment results were compared with the simulation results of the finite difference method. The results show that the maximum simulation error of the average temperature in the operating interval is 11.4%, and the maximum simulation error of thermal error is 16.4%, which verifies the validity and correctness of the method. The thermal characteristic modeling method of the ball screw feed system proposed in this paper has a substantial application value for accurately obtaining the temperature field of the feed system.

ENHANCEMENT OF SPECIALISED VERTICAL TURNING LATHE ACCURACY THROUGH MINIMISATION OF THERMAL ERRORS DEPENDING ON TURNING AND MILLING OPERATIONS

Achieving high workpiece accuracy is a long-term goal of machine tool designers. There are many causes of workpiece inaccuracy, with thermal errors being the most dominant. Indirect compensation (using predictive models) is a promising strategy for reducing thermal errors without increasing machine tool cost. A modelling approach using thermal transfer functions (a dynamic method with a physical basis) embodies the potential to deal with this issue. The method does not require interventions into the machine tool structure, uses a minimum of additional gauges and its modelling and calculation speed is suitable for real-time applications with fine results with up to 80% thermal error reduction. Advanced machine tool thermal error compensation models have been successfully applied on various kinds of single-purpose machines (milling, turning, floor-type, etc.) and implemented directly into their control systems. This research reflects modern trends in machine tool usage and as such is focused on the applicability of the modelling approach to describe specialised vertical turning lathe versatility. The specialised vertical turning lathe is adequately capable of carrying out turning and milling operations. Calibration of the reliable compensation model is a real challenge. The applicability of the approach during immediate switching between turning and milling operations is discussed in more detail.

PREDICTION OF THERMAL ERRORS IN MACHINE TOOLS THROUGH DECOUPLED SIMULATIONS USING GENETIC ALGORITHM AND ARTIFICIAL NEURAL NETWORKS

Thermal errors are one of the major contributors towards positioning discrepancies in machine tools in precision machining. Along with friction and waste heat generated from production processes and internal heat sources, environmental influences around the machine tool create considerable thermal gradients followed by non-linear structural deformations. Efficient quantification of these three contributing sources of thermal errors are required in order to formulate a reliable thermal-error compensation system. The creation of all possible thermal configurations, which a machine tool could be subjected to, is experimentally infeasible and requires complex and time-consuming coupled flow and thermo-structural simulations. This paper presents a new approach in thermal error prediction by using CFD and finite element (FE) simulations to train a three-level interconnected neural network system. The first level essentially decouples flow simulations from thermo-structural simulations using optimal FE node points found using a Genetic Algorithm (GA), which significantly reduces the required training data. The boundary convection data obtained from this level is used in the second level to predict possible thermal configurations of the machine tool, after careful consideration of parameters related to internal heat sources and production processes. The third level maps these thermal configurations onto displacements on the machine tool.